Articles | Volume 17, issue 9
https://doi.org/10.5194/nhess-17-1683-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/nhess-17-1683-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Multi-variable flood damage modelling with limited data using supervised learning approaches
Dennis Wagenaar
CORRESPONDING AUTHOR
Deltares, Boussinesqweg 1, 2629 HV, Delft, the Netherlands
Jurjen de Jong
Deltares, Boussinesqweg 1, 2629 HV, Delft, the Netherlands
Laurens M. Bouwer
Deltares, Boussinesqweg 1, 2629 HV, Delft, the Netherlands
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Latest update: 20 Nov 2024
Short summary
Flood damage models are an important component of cost–benefit analyses for flood protection measures. Currently flood damage models predict the flood damage often only based on water depth. Recently, some progress has been made in also including other variables for this prediction. Data-intensive approaches (machine learning) have been applied to do this. In practice the required data for this are rare. We apply these new approaches on a new type of dataset (combination of different sources).
Flood damage models are an important component of cost–benefit analyses for flood protection...
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